nearest

In [1]:

'''
An example showing nearest point queries, 
primitive volume sampling, oriented bounding boxes, 
and using PointCloud objects for visualization
'''
import trimesh 
import numpy as np

In [2]:

# load a large- ish PLY model with colors    
mesh = trimesh.load('../models/cycloidal.ply')   

In [3]:

# we can sample the volume of Box primitives
points = mesh.bounding_box_oriented.sample_volume(count=10)

In [4]:

# find the closest point on the mesh to each random point
(closest_points,
 distances,
 triangle_id) = mesh.nearest.on_surface(points)
print('Distance from point to surface of mesh:\n{}'.format(distances))

Distance from point to surface of mesh:
[0.00335445 0.08106453 0.18163656 0.35840587 0.09608193 0.00948973
 0.04144551 0.09548092 0.11829215 0.00486006]

In [5]:

# create a PointCloud object out of each (n,3) list of points
cloud_original = trimesh.points.PointCloud(points)
cloud_close    = trimesh.points.PointCloud(closest_points)

# create a unique color for each point
cloud_colors = np.array([trimesh.visual.random_color() for i in points])

# set the colors on the random point and its nearest point to be the same
cloud_original.vertices_color = cloud_colors
cloud_close.vertices_color    = cloud_colors

# create a scene containing the mesh and two sets of points
scene = trimesh.Scene([mesh,
                       cloud_original,
                       cloud_close])

# show the scene wusing 
scene.show()

Out[5]: